Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016 (2016), Article ID 4241293, 8 pages
http://dx.doi.org/10.1155/2016/4241293
Review Article

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

Junyi Li,1,2 Yi-Xue Li,1,2,3,4 and Yuan-Yuan Li2,3,4

1Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai 201203, China
3Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai 201203, China
4Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai 201203, China

Received 14 April 2016; Revised 9 June 2016; Accepted 12 June 2016

Academic Editor: Zhenguo Zhang

Copyright © 2016 Junyi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.